Background

• Diffusion MRI (dMRI) measures tissue properties of white matter, which contains long-range connections between different brain regions.
• Brain-behavior models can be used link neuroimaging features and phenotypes.

Question: How do sets of features derived from different dMRI processing methods compare in model accuracy and variability?

Methods

• Diffusion MRI from 1041 Human Connectome Project participants
• Processed into “tract profiles” using pyAFQ (cite) and “local connectome” features using DSI-Studio (cite).
• LASSO models were run on both tract profiles and local connectome.
• Sparse Group LASSO (SGL) models run on only tract profiles.
• Prediction targets were various cognitive phenotypes.
• Models implemented using R and trained using nested cross-validation and boostrap resampling.

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Conclusions (rough wording)

• The selection of model and feature set might not be influential on the accuracy, but may result in less variable, more interpretable models.
• Tract profiles and local connectome have similar accuracies, but the grouping of tract profiles combined with SGL is good*
• Splitting families across the train/test splits is bad practice, but didn’t have a large effect on the outcome.

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Acknowledgements

• Data were provided by the Human Connectome Project, WU-Minn Consortium

• Krell logo, NIH logo

References

Created with (Allaire et al. 2024)